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Stuart K. Gardiner, William H. Swanson, Steven L. Mansberger; Long- and Short-Term Variability of Perimetry in Glaucoma. Trans. Vis. Sci. Tech. 2022;11(8):3. https://doi.org/10.1167/tvst.11.8.3.
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Test–retest variability in perimetry consists of short-term and long-term components, both of which impede assessment of progression. By minimizing and quantifying the algorithm-dependent short-term variability, we can quantify the algorithm-independent long-term variability that reflects true fluctuations in sensitivity between visits. We do this at locations with sensitivity both < 28 dB (when the stimulus is smaller than Ricco's area and complete spatial summation can be assumed) and > 28 dB (when partial summation occurs).
Frequency-of-seeing curves were measured at four locations of 35 participants with glaucoma. The standard deviation of cumulative Gaussian fits to those curves was modeled for a given sensitivity and used to simulate the expected short-term variability of a 30-presentation algorithm. A separate group of 137 participants was tested twice with that algorithm, 6 months apart. Long-term variance at different sensitivities was calculated as the LOESS fit of observed test–retest variance minus the LOESS fit of simulated short-term variance.
Below 28 dB, short-term variability increased approximately linearly with increasing loss. Long-term variability also increased with damage below this point, attaining a maximum standard deviation of 2.4 dB at sensitivity 21 dB, before decreasing due to the floor effect of the algorithm. Above 30 dB, the observed test–retest variance was slightly smaller than the simulated short-term variance.
Long-term and short-term variability both increase with damage for perimetric stimuli smaller than Ricco's area. Above 28 dB, long-term variability constitutes a negligible proportion of test–retest variability.
Fluctuations in true sensitivity increase in glaucoma, even after accounting for increased short-term variability. This long-term variability cannot be reduced by altering testing algorithms alone.
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